import tensorflow as tf
assert tf.__version__ >= '2.0'
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)
from sklearn.metrics import roc_auc_score, roc_curve, classification_report, confusion_matrix
from tensorflow.keras.callbacks import CSVLogger, ModelCheckpoint, ReduceLROnPlateau
from tensorflow.keras.layers import Dense, Dropout, Input, GlobalAveragePooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.densenet import preprocess_input
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.applications import DenseNet121
from tensorflow.keras.optimizers import Adam
from skimage.transform import resize
import pydicom as dcm, cv2
from PIL import Image
import pandas as pd
import numpy as np
import keras
import math
random_state = 2020
# ROC AUC as a Metric
# Reference: https://stackoverflow.com/questions/41032551/how-to-compute-receiving-operating-characteristic-roc-and-auc-in-keras
def roc_auc(y_true, y_pred):
return tf.compat.v1.py_function(roc_auc_score, (y_true, y_pred), tf.double)
# Average Precision as a Metric
import tensorflow.keras.backend as K
def average_precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
# F1 score as a Metric
# Reference: https://stackoverflow.com/questions/43547402/how-to-calculate-f1-macro-in-keras
def f1_score(y_true, y_pred):
def recall(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))
recall = true_positives / (possible_positives + K.epsilon())
return recall
def precision(y_true, y_pred):
true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))
predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))
precision = true_positives / (predicted_positives + K.epsilon())
return precision
precision = precision(y_true, y_pred)
recall = recall(y_true, y_pred)
return 2*((precision*recall)/(precision+recall+K.epsilon()))
# Data generator
class DataGenerators:
def __init__(self, df_train, df_valid, df_test, batch_size, path,
img_size = (224, 224), class_mode = 'binary',
random_state = 2020):
self.df_train = df_train
self.df_valid = df_valid
self.df_test = df_test
self.batch_size = batch_size
self.img_size = img_size
self.path = path
self.class_mode = class_mode
train_augmenter = ImageDataGenerator(
preprocessing_function = preprocess_input,
rotation_range = 20, width_shift_range = 0.2,
height_shift_range = 0.2, zoom_range = 0.2,
horizontal_flip = True, rescale = 1/255.
)
valid_augmenter = ImageDataGenerator(
preprocessing_function = preprocess_input,
rescale = 1/255.
)
test_augmenter = ImageDataGenerator(
preprocessing_function = preprocess_input,
rescale = 1/255.
)
print('Train Generator Created', '--'*20)
self.train_generator = train_augmenter.flow_from_dataframe(
x_col = 'path',
y_col = 'Target',
dataframe = self.df_train,
batch_size = self.batch_size,
target_size = self.img_size,
directory = self.path,
class_mode = self.class_mode,
seed = random_state,
shuffle = True
)
print('Validation Generator Created', '--'*20)
self.valid_generator = valid_augmenter.flow_from_dataframe(
x_col = 'path',
y_col = 'Target',
dataframe = self.df_valid,
batch_size = self.batch_size,
target_size = self.img_size,
directory = self.path,
class_mode = self.class_mode,
seed = random_state,
shuffle = False
)
print('Test Generator Created', '--'*20)
self.test_generator = test_augmenter.flow_from_dataframe(
x_col = 'path',
y_col = 'Target',
dataframe = self.df_test,
batch_size = self.batch_size,
target_size = self.img_size,
directory = self.path,
class_mode = self.class_mode,
seed = random_state,
shuffle = False
)
self.step_size_train = math.ceil(
self.train_generator.n//self.train_generator.batch_size + 1
)
self.step_size_valid = math.ceil(
self.valid_generator.n//self.valid_generator.batch_size + 1
)
self.step_size_test = math.ceil(
self.test_generator.n//self.test_generator.batch_size + 1
)
